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A review of predictive coding algorithms

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A review of predictive coding algorithms. / Spratling, M. W.

In: Brain and Cognition, Vol. 112, 03.2017, p. 92-97.

Research output: Contribution to journalArticlepeer-review

Harvard

Spratling, MW 2017, 'A review of predictive coding algorithms', Brain and Cognition, vol. 112, pp. 92-97. https://doi.org/10.1016/j.bandc.2015.11.003

APA

Spratling, M. W. (2017). A review of predictive coding algorithms. Brain and Cognition, 112, 92-97. https://doi.org/10.1016/j.bandc.2015.11.003

Vancouver

Spratling MW. A review of predictive coding algorithms. Brain and Cognition. 2017 Mar;112:92-97. https://doi.org/10.1016/j.bandc.2015.11.003

Author

Spratling, M. W. / A review of predictive coding algorithms. In: Brain and Cognition. 2017 ; Vol. 112. pp. 92-97.

Bibtex Download

@article{ac129e30571c445381741def51980a91,
title = "A review of predictive coding algorithms",
abstract = "Predictive coding is a leading theory of how the brain performs probabilistic inference. However, there are a number of distinct algorithms which are described by the term {"}predictive coding{"}. This article provides a concise review of these different predictive coding algorithms, highlighting their similarities and differences. Five algorithms are covered: linear predictive coding which has a long and influential history in the signal processing literature; the first neuroscience-related application of predictive coding to explaining the function of the retina; and three versions of predictive coding that have been proposed to model cortical function. While all these algorithms aim to fit a generative model to sensory data, they differ in the type of generative model they employ, in the process used to optimise the fit between the model and sensory data, and in the way that they are related to neurobiology.",
keywords = "Cortex, Free energy, Neural networks, Predictive coding, Retina, Signal processing",
author = "Spratling, {M. W.}",
year = "2017",
month = mar,
doi = "10.1016/j.bandc.2015.11.003",
language = "English",
volume = "112",
pages = "92--97",
journal = "Brain and Cognition",
issn = "0278-2626",
publisher = "ACADEMIC PRESS INC",

}

RIS (suitable for import to EndNote) Download

TY - JOUR

T1 - A review of predictive coding algorithms

AU - Spratling, M. W.

PY - 2017/3

Y1 - 2017/3

N2 - Predictive coding is a leading theory of how the brain performs probabilistic inference. However, there are a number of distinct algorithms which are described by the term "predictive coding". This article provides a concise review of these different predictive coding algorithms, highlighting their similarities and differences. Five algorithms are covered: linear predictive coding which has a long and influential history in the signal processing literature; the first neuroscience-related application of predictive coding to explaining the function of the retina; and three versions of predictive coding that have been proposed to model cortical function. While all these algorithms aim to fit a generative model to sensory data, they differ in the type of generative model they employ, in the process used to optimise the fit between the model and sensory data, and in the way that they are related to neurobiology.

AB - Predictive coding is a leading theory of how the brain performs probabilistic inference. However, there are a number of distinct algorithms which are described by the term "predictive coding". This article provides a concise review of these different predictive coding algorithms, highlighting their similarities and differences. Five algorithms are covered: linear predictive coding which has a long and influential history in the signal processing literature; the first neuroscience-related application of predictive coding to explaining the function of the retina; and three versions of predictive coding that have been proposed to model cortical function. While all these algorithms aim to fit a generative model to sensory data, they differ in the type of generative model they employ, in the process used to optimise the fit between the model and sensory data, and in the way that they are related to neurobiology.

KW - Cortex

KW - Free energy

KW - Neural networks

KW - Predictive coding

KW - Retina

KW - Signal processing

UR - http://www.scopus.com/inward/record.url?scp=84955274422&partnerID=8YFLogxK

U2 - 10.1016/j.bandc.2015.11.003

DO - 10.1016/j.bandc.2015.11.003

M3 - Article

AN - SCOPUS:84955274422

VL - 112

SP - 92

EP - 97

JO - Brain and Cognition

JF - Brain and Cognition

SN - 0278-2626

ER -

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